University of Groningen Collective action and network change Takacs, Karoly; Janky, Bela; Flache, Andreas Published in: Social Networks DOI: 10.1016/j.socnet.2008.02.003 IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below. Document Version Publisher's PDF, also known as Version of record Publication date: 2008 Link to publication in University of Groningen/UMCG research database Citation for published version (APA): Takacs, K., Janky, B., & Flache, A. (2008). Collective action and network change. Social Networks, 30(3), 177-189. https://doi.org/10.1016/j.socnet.2008.02.003 Copyright Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons). 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Download date: 28-09-2021 Social Networks 30 (2008) 177–189 Contents lists available at ScienceDirect Social Networks journal homepage: www.elsevier.com/locate/socnet Collective action and network change Karoly´ Takacs´ a,b,∗,Bela´ Janky c, Andreas Flache b a Corvinus University of Budapest, Institute of Sociology and Social Policy, Hungary b University of Groningen, Faculty of Behavioral and Social Sciences, Department of Sociology/ICS, The Netherlands c Budapest University of Technology and Economics, Department of Sociology and Communication, Hungary article info abstract Keywords: Network models of collective action commonly assume fixed social networks in which ties influence Collective action participation through social rewards. This implies that only certain ties are beneficial from the view Social dilemmas of individual actors. Accordingly, in this study we allow that actors strategically revise their relations. Social networks Moreover, in our model actors also take into account possible network consequences in their participation Network dynamics choices. To handle the interrelatedness of networks and participation, we introduce new equilibrium Social control Structural balance concepts. Our equilibrium analysis suggests that structures that tend to segregate contributors from free Local interaction games riders are stable, but costless network change only promotes all-or-nothing participation and complete networks. © 2008 Elsevier B.V. All rights reserved. 1. Introduction work members have strong incentives to insulate themselves from pressures to contribute. Based on different behavioral assumptions, Why and under which social conditions are groups successful in other theoretical studies supported the view that dense networks mobilizing collective action? Voluntary participation in collective may sometimes undermine rather than facilitate the enforcement actions, such as fund raising, strike movements or political upris- of contribution (Flache and Macy, 1996; Flache, 1996, 2002; Kitts et ing, seems often to contradict the narrowly defined self-interest al., 1999). The theoretical argument focuses on the desire of actors of the participants. Yet, empirical examples of successful mobiliza- to obtain social rewards from other group members including those tion abound. Students of collective action point to social networks who “free ride”. The desire to retain relationships with or attain as an important answer (see, e.g., Oberschall, 1973; Tilly, 1978; behavioral confirmation from free riders may often compromise Oliver, 1984; McAdam, 1986; Marwell et al., 1988; Gould, 1993a; actors’ willingness to exert social control towards contribution, Sandell and Stern, 1998; Chwe, 1999, 2000; and for an overview especially in a closely knit network. Diani, 2003a). Dense networks of communication and interac- To clarify under what conditions networks are positively or neg- tion between prospective participants may greatly facilitate group atively related to collective action success, models of collective mobilization (Opp and Gern, 1993; Gould, 1993b; Marwell and action need to incorporate explicitly how individual actors make Oliver, 1993). The view that social networks facilitate collective purposive decisions to use their social relations to foster their goals, action relies on the assumption that individual network mem- be it to enforce compliance or to resist peer pressure. But studies bers have a “regulatory interest” (Heckathorn, 1988; Kitts, 2006) that combine positive and negative effects of social ties on cooper- to enforce others’ contribution to the collective action. Particularly ation (e.g., Oberschall, 1994; Heckathorn, 1996; Takacs,´ 2001)have in dense or closed networks, actors can effectively employ their neglected a crucial implication of this perspective. Purposive action social ties for this purpose (Hechter, 1987; Coleman, 1990)both implies that network members in a collective action situation not because group members have more information about one another only use existing ties to attain their goals, but they may also make and because they have more social means to provide rewards for or break ties if this serves their purposes. compliance or punish deviance. In general, most models of collective action that address social But it has also been argued that network ties have a “double network effects implicitly assume that there is a fixed set of edge” (Flache, 1996). Heckathorn (1996) has pointed out how peer interpersonal relations that do not change over time. Relational pressure may take the form of “oppositional control”, when net- ties are in these studies exogenously given and at most, static comparisons are made. We argue that the relationship between collective action and social networks cannot be properly stud- ∗ ied without addressing endogenous network change driven by Corresponding author at: Corvinus University of Budapest, Institute of Sociology and Social Policy, H-1093 Budapest, Kozrakt¨ ar´ u. 4–6, Hungary. individual interests. Regulatory interests do not necessarily lead E-mail address: [email protected] (K. Takacs).´ to enforcement, but possibly also to avoidance of unpleasant 0378-8733/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.socnet.2008.02.003 178 K. Takacs´ et al. / Social Networks 30 (2008) 177–189 control and optimization of contacts. For illustration, consider the understand collective action and stability of social networks. The situation of a defector who is particularly sensitive to conformity main added value of this study for the literature on network for- pressure and who has her ties mostly with compliant group mem- mation is the development and application of such equilibrium bers. In a static network, it is likely that pressure to contribute concepts to n-person games. brings this defector back into line. But if networks can change, the Another line of work explicitly combines collective action with defector faces an incentive to maximize the number of ties she has network change (Kitts et al., 1999), but uses “backward-looking” with other defectors and thus break ties with cooperators and build models of structural learning that describe the process by which new ties with other defectors. If the latter mechanism prevails, individuals strengthen ties that are beneficial for them and aban- the outcome on the collective level may be a disconnected net- don ties with negative experience (Macy et al., 2003). These models work with a deviant clique on which cooperators cannot effectively do not assume that decision making is strategic and purposive in impose peer pressure. But if the first mechanism is more important, the sense that individual actors weigh the costs and benefits of the result may be a network in which free riders are effectively changing their contribution behavior against the costs and benefits sanctioned by their compliant peers. In this example, models of of making or breaking relationships, or a combination of both. This collective action that neglect endogenous network changes might makes it difficult to address effects of exogenous constraints, such arrive at questionable conclusions. as given access structures of networks or communication costs, In this study we emphasize that networks change over time and within the structural learning framework. that this is also reflected in how people behave in collective action In this paper, we propose a game theoretical model of collective (Kim and Bearman, 1997; Diani, 2003b; Osa, 2003; Gould, 2003). action in dynamic networks and arrive at equilibrium predictions. Besides being driven by independent network dynamics, networks Our main innovation is that we incorporate into a collective action may also change because of the internal determinants of collective framework the possibility of tie formation and deletion, and model action. People might choose their structural